Instead of converting the model's output to an ID prediction Lee et al. use the activations of the model to generate class-conditional Gaussian distributions [1]} (Mahalanobis).
Doing so, they model OOD images as unlikely activations, i.e. having a large Mahalanobis distance to the modeled class means.
They motivate using the Mahalanobis distance between the mean representation of a class and the input in the feature space, instead of performing OOD detection in the label space due to “label overfitting”, i.e. that the model predictions are conditioned on the training labels.
